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      Biochemical phenotyping unravels novel metabolic abnormalities and potential biomarkers associated with treatment of GLUT1 deficiency with ketogenic diet

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          Abstract

          Global metabolomic profiling offers novel opportunities for the discovery of biomarkers and for the elucidation of pathogenic mechanisms that might lead to the development of novel therapies. GLUT1 deficiency syndrome (GLUT1-DS) is an inborn error of metabolism due to reduced function of glucose transporter type 1. Clinical presentation of GLUT1-DS is heterogeneous and the disorder mirrors patients with epilepsy, movement disorders, or any paroxysmal events or unexplained neurological manifestation triggered by exercise or fasting. The diagnostic biochemical hallmark of the disease is a reduced cerebrospinal fluid (CSF)/blood glucose ratio and the only available treatment is ketogenic diet. This study aimed at advancing our understanding of the biochemical perturbations in GLUT1-DS pathogenesis through biochemical phenotyping and the treatment of GLUT1-DS with a ketogenic diet. Metabolomic analysis of three CSF samples from GLUT1-DS patients not on ketogenic diet was feasible inasmuch as CSF sampling was used for diagnosis before to start with ketogenic diet. The analysis of plasma and urine samples obtained from GLUT1-DS patients treated with a ketogenic diet showed alterations in lipid and amino acid profiles. While subtle, these were consistent findings across the patients with GLUT1-DS on ketogenic diet, suggesting impacts on mitochondrial physiology. Moreover, low levels of free carnitine were present suggesting its consumption in GLUT1-DS on ketogenic diet. 3-hydroxybutyrate, 3-hydroxybutyrylcarnitine, 3-methyladipate, and N-acetylglycine were identified as potential biomarkers of GLUT1-DS on ketogenic diet. This is the first study to identify CSF, plasma, and urine metabolites associated with GLUT1-DS, as well as biochemical changes impacted by a ketogenic diet. Potential biomarkers and metabolic insights deserve further investigation.

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          Organization of GC/MS and LC/MS metabolomics data into chemical libraries

          Background Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSn detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Qu antify I ndividual C omponents in a S ample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites. Results LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library. Conclusion The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.
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            High Resolution Mass Spectrometry Improves Data Quantity and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Profiling Metabolomics

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              Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism

              Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs. Electronic supplementary material The online version of this article (doi:10.1007/s10545-015-9843-7) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Writing – review & editing
                Role: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                29 September 2017
                2017
                : 12
                : 9
                : e0184022
                Affiliations
                [1 ] Department of Translational Medicine, Sector of Pediatrics, Federico II, University, Naples, Italy
                [2 ] Telethon Institute of Genetics and Medicine, Pozzuoli, Naples, Italy
                [3 ] Department of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX, United States of America
                [4 ] Greenwood Genetic Center, Greenwood, South Carolina, United States of America
                [5 ] Department of Medical Genetics & Genomic Medicine, Saint Peter’s University Hospital, New Brunswick, New Jersey, United States of America
                [6 ] Department Brain and Behavioral Sciences Fondazione IRCCS C, Mondino, Italy
                [7 ] Pediatric Neurology and Muscular Diseases Unit, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, ‘G, Gaslini’ Institute, Genova, Italy
                [8 ] Metabolon, Morrisville, North Carolina, United States of America
                Mayo Clinic Rochester, UNITED STATES
                Author notes

                Competing Interests: SHE and TD are members of the Department of Molecular and Human Genetics at Baylor College of Medicine, which as part of a joint venture with Miraca Holdings (Baylor Genetics) offers several clinical tests on a fee-for-service basis, but this entity in no way conflicts with the research reported here. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. AK is an employee of Metabolon, Inc. and, as such, has affiliations with or financial involvement with Metabolon, Inc. AK has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

                Author information
                http://orcid.org/0000-0002-1400-8519
                Article
                PONE-D-17-17282
                10.1371/journal.pone.0184022
                5621665
                28961260
                0b2401e1-c074-4735-9396-fda73954c7ac
                © 2017 Cappuccio et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 May 2017
                : 30 July 2017
                Page count
                Figures: 2, Tables: 7, Pages: 15
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Biology and Life Sciences
                Nutrition
                Diet
                Medicine and Health Sciences
                Nutrition
                Diet
                Biology and Life Sciences
                Biochemistry
                Lipids
                Lipid Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Lipid Metabolism
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Carbohydrate Metabolism
                Glucose Metabolism
                Biology and Life Sciences
                Anatomy
                Body Fluids
                Cerebrospinal Fluid
                Medicine and Health Sciences
                Anatomy
                Body Fluids
                Cerebrospinal Fluid
                Biology and Life Sciences
                Physiology
                Body Fluids
                Cerebrospinal Fluid
                Medicine and Health Sciences
                Physiology
                Body Fluids
                Cerebrospinal Fluid
                Biology and Life Sciences
                Anatomy
                Nervous System
                Cerebrospinal Fluid
                Medicine and Health Sciences
                Anatomy
                Nervous System
                Cerebrospinal Fluid
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolomics
                Biology and Life Sciences
                Biochemistry
                Lipids
                Fatty Acids
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Amino Acid Metabolism
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Carbohydrates
                Monosaccharides
                Glucose
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